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How can you determine the best size for a data warehouse?

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Data warehousing is a process of collecting, integrating, and analyzing data from various sources to support business intelligence and decision making. A data warehouse is a centralized repository that stores the data in a structured and organized way. One of the challenges of data warehousing is to determine the best size for a data warehouse, which depends on several factors such as the data volume, the data quality, the data usage, and the data architecture. In this article, we will explore some of the methods and tools that can help you estimate the optimal size for your data warehouse.

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Rhayar Mascarello

Senior Data Engineer | Generative AI Engineer | Azure Solutions Architect Expert | Databricks

1 Data volume

The data volume is the amount of data that you need to store in your data warehouse. It is influenced by the number and type of data sources, the frequency and granularity of data extraction, the level of data compression, and the retention period of data. To estimate the data volume, you can use formulas or calculators that take into account these factors and multiply them by the average size of each data row or column. For example, if you have 10 sources that generate 100 MB of data per day, and you want to keep the data for 5 years, you can use the formula: 10 x 100 MB x 365 days x 5 years = 182.5 GB.

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Artemakis A.

AI/Automation Architect | Database Expert | Former Microsoft MVP | Scaled Scrum Master | Published Author & Speaker
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Determining the optimal size for a data warehouse requires a nuanced approach that considers both current and future data volume. Assess historical data growth rates and project future data expansion to estimate storage needs accurately. Additionally, factor in scalability options to accommodate potential increases in data volume over time without compromising performance or reliability.

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2 Data quality

The data quality is the degree to which the data in your data warehouse is accurate, consistent, complete, and relevant. It is affected by the quality of the data sources, the data cleansing and transformation processes, the data validation and verification procedures, and the data governance policies. To improve the data quality, you can use techniques such as data profiling, data cleansing, data deduplication, data standardization, and data enrichment. These techniques can also reduce the size of your data warehouse by eliminating errors, duplicates, and irrelevant data.

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Rhayar Mascarello

Senior Data Engineer | Generative AI Engineer | Azure Solutions Architect Expert | Databricks
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To determine the best size for a data warehouse, focus on capacity planning, which involves forecasting data volume growth over time, considering factors like the frequency of data refreshes and the expected lifespan of the data warehouse.

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Alexandre Soubihe Previdello

Analista de negócios| Gestor de Custos | Business Intelligence | Data Science | Power BI | Python | Big Data | ETL | Dashboards | KPI | BPM | Gest?o Estratégica | Machine Learning |Metodologias ágeis | Scrum
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A qualidade dos dados é uma das etapas mais importantes, pois se os dados n?o tiverem consistidos, validados , íntegros esses poder?o gerar insights errados e posteriormente tomadas de decis?es equivocadas... A qualidade, um outro fator importante é a Granulidade, isso precisa estar bem alinhado com o cliente. A qualidade precisa ser encarada com muita aten??o pois sem ela n?o tem como ter a " inteligência de negócio"

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Artemakis A.

AI/Automation Architect | Database Expert | Former Microsoft MVP | Scaled Scrum Master | Published Author & Speaker
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When determining the best size for a data warehouse, data quality is paramount. Prioritize quality over quantity by focusing on relevant, accurate, and reliable data sources. Implement robust data cleansing and validation processes to ensure data integrity and eliminate duplicates or inconsistencies. By maintaining high data quality standards, organizations can optimize storage space and enhance the effectiveness of analytics and reporting.

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3 Data usage

The data usage is the frequency and intensity of data access and analysis by the users of your data warehouse. It is determined by the number and type of users, the business requirements and objectives, the analytical tools and methods, and the performance expectations and SLAs. To measure the data usage, you can use metrics such as queries per day, query response time, concurrency, and user satisfaction. These metrics can help you optimize the size of your data warehouse by balancing the trade-off between storage and performance.

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Artemakis A.

AI/Automation Architect | Database Expert | Former Microsoft MVP | Scaled Scrum Master | Published Author & Speaker
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Understanding data usage patterns is essential for sizing a data warehouse effectively. To this end, analyze usage metrics such as query frequency, data access patterns, and user behaviours to identify peak usage periods and data access trends. Tailor the size and capacity of the data warehouse to accommodate anticipated usage demands, ensuring optimal performance and responsiveness for end-users across various analytical and reporting tasks.

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4 Data architecture

The data architecture is the design and structure of your data warehouse, which includes the data model, the data schema, the data partitioning, the data indexing, and the data compression. It is influenced by the data characteristics, the data relationships, the data analysis patterns, and the data warehouse platform. To choose the best data architecture, you can use methods such as star schema, snowflake schema, dimensional modeling, columnar storage, and partitioning. These methods can affect the size of your data warehouse by enhancing the data organization, accessibility, and efficiency.

Determining the best size for a data warehouse is not a one-time task, but a continuous process that requires regular monitoring and adjustment. By using the methods and tools discussed in this article, you can estimate and optimize the size of your data warehouse according to your data volume, data quality, data usage, and data architecture. This way, you can ensure that your data warehouse meets your business needs and delivers value to your organization.

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Artemakis A.

AI/Automation Architect | Database Expert | Former Microsoft MVP | Scaled Scrum Master | Published Author & Speaker
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The architecture of the data warehouse plays a crucial role in determining its size requirements. Choose a scalable and flexible architecture that can accommodate evolving data needs and usage patterns. Consider factors such as storage technologies, database design, and data partitioning strategies to optimize resource utilization and performance. Additionally, leverage cloud-based solutions for elasticity and cost-effectiveness, allowing for seamless scalability and adaptation to changing business requirements.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you determine the best size for a data warehouse?

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1 Data volume

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4 Data architecture

5 Here’s what else to consider

Database Development

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How can you determine the best size for a data warehouse?

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1 Data volume

2 Data quality

3 Data usage

4 Data architecture

5 Here’s what else to consider

Database Development

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